Abstract.The dual frequency signals from the GPS satellites recorded at Rajkot (22.29 • N, 70.74 •

The first regional total electron content (TEC) model over the entire African region (known as AfriTEC model) using empirical observations is developed and presented. Artificial neural networks were used to train TEC observations obtained from Global Positioning System receivers, both on ground and onboard the Constellation Observing System for Meteorology, Ionosphere, and Climate satellites for the African region from years 2000 to 2017. The neural network training was implemented using inputs that enabled the networks to learn diurnal variations, seasonal variations, spatial variations, and variations that are connected with the level of solar activity, for quiet geomagnetic conditions (−20 nT ≤ Dst ≤ 20 nT). The effectiveness of three solar activity indices (sunspot number, solar radio flux at 10.7-cm wavelength [F10.7], and solar ultraviolet [UV] flux at 1 AU) for the neural network trainings was tested. The F10.7 and UV were more effective, and the F10.7 was used as it gave the least errors on the validation data set used. Equatorial anomaly simulations show a reduced occurrence during the June solstice season. The distance of separation between the anomaly crests is typically in the range from about 11.5 ± 1.0°to 16.0 ± 1.0°. The separation is observed to widen as solar activity levels increase. During the December solstice, the anomaly region shifts southwards of the equinox locations; in year 2012, the trough shifted by about 1.5°and the southern crest shifted by over 2.5°. Key Points:• The first regional TEC model over the entire African region using empirical observations is developed • The model offers opportunities to conduct high spatial resolution investigations over the African region • EIA occurrence is reduced during the June solstice, and the anomaly region shifts southwards during December solstice Data used in this work include GPS data, indices for solar and geomagnetic activities, and data from ionospheric models used to comparatively verify/validate the model developed. Figure 7. RMSE variations for predictions of the AfriTEC model using the test data set under conditions of varying (a) latitudes, (b) F10.7 values, (c) local times, and (d) days of the years.Figure 11. (a) Sample TEC profile for longitude 20°E illustrating the determination of anomaly crest and trough locations. The illustrated profile is for the March equinox day of year 2012. (b) to (d) are spatial simulations of TEC from the AfriTEC model for 13:00 UT of day number 79 of years 2009, 2012, and 2014, respectively. The F10.7 values are respectively 68, 101, and 150.

No abstract

The Sun is the major driver of space weather events, and as a result, most applications requiring modeling/forecasting of space weather phenomena depend largely on the activities of Sun. Accurate modeling of solar activity parameters like the sunspot number (SSN) is therefore considered significant for the quantitative modeling of space weather phenomena. Sunspot number forecasts are applied in ionospheric models like the International Reference Ionosphere model and in several other projects requiring prediction of space weather phenomena. A method called Hybrid Regression‐Neural Network that combines regression analysis and neural network learning is used for forecasting the SSN. Considering the geomagnetic Ap index during the end of the previous cycle (known as the precursor Ap index) as a reliable measurement, we predict the end of solar cycle 24 to be in March 2020 (±7 months), with monthly SSN 5.4 (±5.5). Using an estimated value of precursor Ap index as 5.6 nT for solar cycle 25, we predict the maximum SSN to be 122.1 (±18.2) in January 2025 (±6 months) and the minimum to be 6.0 (±5.5) in April 2031 (±5 months). We found from the model that on changing the assumed value of precursor Ap index (5.6 nT) by ±1 nT, the predicted peak of solar cycle 25 changes by about 11 sunspots for every 1‐nT change in the assumed precursor Ap index.

Solar X-ray Spectrometer (SOXS), the first space-borne solar astronomy experiment of India was designed to improve our current understanding of X-ray emission from the Sun in general and solar flares in particular. SOXS mission is composed of two solid state detectors, viz., Si and CZT semiconductors capable of observing the full disk Sun in X-ray energy range of 4-56 keV. The X-ray spectra of solar flares obtained by the Si detector in the 4-25 keV range show evidence of Fe and Fe/Ni line emission and multi-thermal plasma. The evolution of the break energy point that separates the thermal and non-thermal processes reveals increase with increasing flare plasma temperature. Small scale flare activities observed by both the detectors are found to be suitable to heat the active region corona; however their location appears to be in the transition region.